The present invention relates to instruments for the non-invasive quantitative measurement of constituents, such as blood glucose levels in blood. More specifically, this invention provides improvements in methods and apparatus for near-infrared quantitative analysis.
The use of quantitative near-infrared (xe2x80x9cNIRxe2x80x9d) analysis for the determination of chemical and/or physical characteristics of products is relatively well known in the art. See xe2x80x9cAn Introduction to Near Infrared Quantitative Analysis,xe2x80x9dby Robert D. Rosenthal, presented at the 1977 Annual Meeting of the American Association of Cereal Chemists, (1978). See also U.S. Pat. No. 4,286,327 issued to Rosenthal et al. on Aug. 25, 1981.
Another well-known application of NIR analysis relates to the quantitative measurement of analytes in mammals, such as quantitative analysis of glucose in the blood. Information concerning the chemical composition of blood is widely used to assess the health characteristics of both people and animals. More specifically, analysis of the glucose content of blood provides an indication of the current status of the metabolism. Blood analysis, by the detection of above or below normal levels of various substances, also provides a direct indication of the presence of certain types of diseases and dysfunctions.
In particular, the non-invasive NIR quantitative measurement apparatus has particular application for use by diabetics in monitoring the level of glucose in the blood. See U.S. Pat. No. 5,028,787, Rosenthal et al., issued Jul. 2, 1991, the subject matter of which is hereby incorporated by reference in its entirety.
Quantitative NIR analysis is based on the principle that most organic (and some inorganic) substances absorb radiation in the near-infrared range, with the different substances having different absorption characteristics over specific NIR wavelength ranges. These different characteristics are then used to formulate specific measurement algorithms for obtaining quantitative information regarding the presence of such substances in the subject sample, product or patient.
The above-cited ""327 patent teaches the use of infrared emitting diodes (IREDs) as sources of near-infrared radiation. As shown in FIG. 1, a plurality (eight in the figure) of IREDs 10 is arranged over a sample WS to be illuminated for quantitative analysis. Near-infrared radiation emitted from each IRED impinges upon an accompanying optical filter 12. Each optical filter 12 is a narrow bandpass filter that passes NIR radiation at a different wavelength, and light baffles 14 are provided between IREDs to prevent an IRED""s light from being transmitted through an adjacent filter. In the illustrated example, the sample WS is held in a holder 16 having a transparent bottom 18. NIR radiation passing through the sample and the holder is detected by a detector 20, such as a silicon photodetector, and converted to an electrical signal. The electrical signal is processed by processing circuitry, including an amplifier 22, logarithmic amplifier 23, and analog-to-digital converter 24, and inputted to microprocessor 11. The microprocessor processes the data from the detector, using preprogrammed algorithms to obtain a quantitative measurement of the analytes of interest in the sample, and outputs the result on a display 26.
FIG. 2 illustrates another known NIR instrument for non-invasive measurement of blood analytes, as disclosed in U.S. Pat. No. 5,077,476, issued to Rosenthal on Dec. 31, 1991. The subject matter of the ""476 patent is also incorporated by reference herein in its entirety. In brief summary, the instrument 1 uses a number of IREDs (50, 60 as shown in FIG. 2) for irradiating a body part, such as the finger, with NIR radiation at selected wavelengths. Narrow bandpass optical filters 160 and 170 are positioned at the output of the IREDs to pass NIR radiation at a selected wavelength. The radiation passes through a window 140, through the subject, and is detected by a detector 80. A light baffle 40 is provided to isolate the various IREDs to prevent radiation from one IRED passing through the optical filter associated with a different IRED.
The detector 80 outputs a signal to a microprocessor 100 through amplifier 90. The microprocessor calculates the concentration of analytes at issue (such as blood glucose) using preprogrammed algorithms and outputs the results to a display device 180. In this instrument, timing and control circuitry 110 is provided to sequentially and individually turn on and off each IRED, one at a time, so that the absorption by the blood analytes and other substances may be measured at each particular wavelength specified in the measurement algorithm.
In almost all known NIR measurement devices, such as those described above, the preprogrammed algorithms for interpreting the quantitative measurements are based upon Beer""s Law, which provides that the amount of chemical constituent(s) to be measured is linearly related to D, where                               D          =                      log            ⁢                          xe2x80x83                        ⁢                          (                              1                I                            )                                      ,                            (                  Equation          ⁢                      xe2x80x83                    ⁢          1                )            
in which I can be the fraction of light that is either transmitted through an object, reflected off an object or interacted with the object. This linear concept has worked quite well in many NIR measurement applications. For example, U.S. Pat. No. 4,928,014, issued May 22, 1990 to Rosenthal, provides for the measurement of body fat in the human body using an NIR measurement device.
These historical successes using Beer""s Law have been in applications where the change in constituent concentration has been modest. For example, in the measurement of body fat, as taught by the ""014 patent, the percentage of body fat varies from a minimum in the neighborhood of 10% to a maximum of approximately 40%, a four to one change. In contrast, a much larger range of concentration change occurs in certain blood analytes. For example, blood glucose molality can vary from 20 mg/dL to more than 500 mg/dL, a change of twenty-five to one. Over such large ranges of values, the basic assumption in Beer""s Law of linearity may be invalid. As a result, conventional Multiple Linear Regression (xe2x80x9cMLRxe2x80x9d) or factor analysis does not provide sufficient accuracy to be meaningful. In fact, the D values, as defined in Equation 1, from the NIR measurement of blood glucose values generally have a highly nonlinear relationship.
Attempts to use an NIR device to measure blood glucose have encountered many problems. The NIR measurement devices must be calibrated for each individual user, with more accurate calibrations resulting in more accurate readings. It is generally tedious and time consuming to accurately calibrate the NIR measurement device. During the calibration, measurements of the NIR device are compared with other measurements known to be accurate. The NIR device is then adjusted to produce measurements that correspond with the measurements known to be accurate. The conventional approach for the NIR calibration is to evaluate absorption wavelengths and reference wavelengths. The absorption wavelengths are the portions of the electromagnetic spectrum with high absorbance due to the particular constituents of interest, and reference wavelengths are the portions of the spectrum that are insensitive to the particular constituents. However, in the very near infrared portion of the spectrum, from 700 nm to 1100 nm, the absorptions are quite broad, and thus, independent selection of wavelengths is difficult. Moreover, in the case of in vivo measurement of various blood analytes through a body part, the problem is even more difficult. Approximately ninety-nine percent of the organic content of a finger is due to the presence of water, fat and muscle (protein). The remaining 1 percent includes all of the blood analytes to be measured, as well as other materials. Even further complicating the NIR measurement is that blood analytes, such as blood glucose, are very weak NIR absorbers. There thus exists the overall challenge of measuring a very minute quantity of a substance that is a weak absorber in the presence of very strong absorbers in a portion of the spectrum where individual absorption characteristics are not easily recognizable.
Thus, there presently exists a need for an improved methodology to produce an accurate NIR measurement over a large range of possible values. Furthermore, the methodology needs to allow for the calibration of an NIR instrument to achieve the accurate, non-invasive measurement of blood analytes, especially weak absorbers such as blood glucose.
Accordingly, there is further need for a methodology that is able to calibrate an NIR blood glucose measurement unit so that the calibration is usable over a wide range of values. More specifically, the methodology needs to be accurate at the low and high glucose values so that NIR measurement device is of value in diagnosing and preventing hypoglycemia and hyperglycemia. The methodology needs to provide sufficient accuracy for medical use, so the method should provide a calibration that produces accuracy at least equal to the home finger stick meters used during the Diabetes Control and Complications Trial (DCCT). Furthermore, the methodology needs to produce a calibration that is robust and, therefore, able to accurately predict blood glucose over a reasonably extended period of time and to minimize the need for recalibration.
In response to these needs, the present invention provides a method that better analyzes the near infrared (xe2x80x9cNIRxe2x80x9d) measurement data terms to achieve a better calibration of the NIR measurement device to the specific user. The method uses the codependence of data to extract additional information from the NIR measurement device. Codependence of the data terms is represented in cross-product terms. The cross-product terms are formed in the first step of the method by using the data terms from the NIR measurement device. Then in the second step, statistical analysis is performed using the data terms and the cross-product terms to find a desirable calibration. During this second step, randomly generated sets of the data terms and the cross-product terms are tested to find a best solution, according to prespecified conditions. More specifically, the invention includes a means to select randomly a desirable combination of data and cross-product terms to produce a good calibration to improve the predictability of blood analyte measurements.